A Heterogeneous Resource Management Solution Combining Hybrid Optimization Algorithms and Cloud-Edge-Device Architecture

With the development of 5G IoT and cloud collaboration technology, traditional industrial manufacturing is gradually transforming and upgrading to intelligent manufacturing, resource scheduling plays a crucial role in largescale distributed systems, showing great upgrade potential for intelligent manufacturing and other fields. However, centralized cloud computing can no longer meet the demand of network latency and fast response, and there are situations such as low resource scheduling efficiency and resource waste, etc. Especially in the Telematics scenario, the differences in resource scheduling efficiency and performance characteristics have become a key reason for restricting the promotion of Telematics. This paper proposes a new resource scheduling algorithm, collaborative mechanism to optimize the decision-making model, optimize the data transmission and storage mode, improve the data processing and application efficiency, accelerate the decision-making and response speed, and effectively manage and flexibly schedule the computing resources of each node to improve the efficiency of resource utilization, realize the optimal utilization of resources, multi-level deployment of computation, storage, network, and algorithmic resources, and ensure the quality of service to users, this research provides a better solution to improve the scheduling rate of computing resources in the car networking scenario and promotes the digital transformation process of manufacturing industry.